Short-Form Videos and Mental Health: A Knowledge-Guided Multimodal Neural Topic Model

Core Concepts

The author presents a novel method, the Knowledge-guided Multimodal NTM, to predict the depressive impact of short-form videos on viewers. By incorporating medical knowledge into the model, they aim to improve prediction accuracy and provide practical implications for video platforms.

Abstract

The study addresses concerns about short-form videos' impact on mental health by proposing a predictive model that outperforms existing benchmarks. It leverages Neural Topic Models guided by medical knowledge to predict depressive impacts accurately from video content and comments. Short-form video platforms have raised worries about their influence on viewers' mental well-being. The study introduces a novel approach to predict depressive impacts using a Knowledge-guided Multimodal Neural Topic Model. By incorporating medical ontology, the model aims to optimize recommendations and viewer discretion based on predicted depressive impacts. Existing studies have highlighted the association between short-form videos and increased depressive symptoms among users. The proposed method extends beyond traditional NTMs by integrating deep learning architectures for improved performance in predicting depressive impacts from video content. The research contributes to computational design science in Information Systems by offering a predictive analytics model tailored for short-form videos' mental health implications. By leveraging seeded NTMs with medical knowledge guidance, the study provides insights into optimizing platform recommendations and viewer discretion.

Stats

TikTok reaches over 1.5 billion monthly active users. Douyin datasets used in empirical analyses. Proposed method outperforms state-of-the-art benchmarks. Extensive empirical analyses demonstrate effectiveness across platforms. Topics linked to depressive impact discovered from videos.

Quotes

"Short-form videos are brewing a dangerous breeding ground for mental disorders." - Schlott 2022 "Predicting short-form videos’ depressive impact lends multi-faceted practical implications." - Author "Our method can help platforms understand videos’ mental impacts." - Author

Key Insights Distilled From

Short-Form Videos and Mental Health

by Jiaheng Xie,... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2402.10045.pdf

Deeper Inquiries

How can short-form video platforms balance freedom of speech with minimizing depressive impacts?

Short-form video platforms can balance freedom of speech with minimizing depressive impacts by implementing certain strategies. Firstly, they can provide viewer discretion options that disclose topics in videos related to depressive impacts. This allows viewers to make informed decisions about the content they consume while still upholding their right to access a variety of content. Platforms can also adjust their recommendation algorithms to limit the exposure of potentially depressive-impacting videos to users who may be more vulnerable. By incorporating these measures, platforms can promote responsible content consumption without infringing on freedom of speech.

What ethical considerations should be taken into account when implementing predictive models for mental health?

When implementing predictive models for mental health, several ethical considerations must be taken into account. Firstly, ensuring data privacy and confidentiality is crucial as mental health data is sensitive and personal. Transparency about how the predictive model works and what data it uses is essential for building trust with users. Additionally, bias mitigation is important to prevent any discriminatory outcomes or reinforcing existing biases in mental health care. It's also vital to obtain informed consent from individuals before using their data for prediction purposes.

How might emerging social media trends influence the topics related to depressive impacts in short-form videos?

Emerging social media trends can influence the topics related to depressive impacts in short-form videos by introducing new triggers or stressors that may affect viewers' mental well-being. For example, challenges like body image issues or cyberbullying trends could lead creators to produce content that inadvertently contributes to feelings of inadequacy or low self-esteem among viewers. Keeping abreast of these trends and understanding their potential impact on mental health is crucial for platforms looking to mitigate negative effects on users' well-being through proactive measures such as topic disclosure and algorithm adjustments based on emerging themes related to depression risk factors.